This is a textbook for medical students covering orthopaedics, trauma and rheumatology, offering both core information regarding what the student needs to know about these subject areas and an extensive series of cases with questions and answers that illustrate the thinking behind common everyday practice.
From the series you know and trust comes Blueprints in Neurology! At last the perfect complement to your core subject areas, this text gives you the high-yield facts you need and the same carefully organized format that you recognize. When used in conjunction with the other titles in the Blueprints series, you'll receive a complete review for the USMLE Step 2&3 Exams.
Blueprints in Neurology gives you complete coverage of the most important and common topics in neurology. Contains the information needed for your Neurology clinical rotation.
The last decade has brought dramatic changes in the way that researchers analyze economic and financial time series. This book synthesizes these recent advances and makes them accessible to first-year graduate students. James Hamilton provides the first adequate text-book treatments of important innovations such as vector autoregressions, generalized method of moments, the economic and statistical consequences of unit roots, time-varying variances, and nonlinear time series models.
The material covered in the book includes concepts of linear regression, univariate and multivariate time series modelling and their implementation in EViews. Chapter 1 briefly introduces commands, structure and programming language of the EViews package. Chapter 2 provides an overview of the regression analysis and its inference. Chapters 3 to 5 cover some topics of univariate time series analysis including linear models, GARCH models of volatility, unit root tests. Chapter 6 introduces modelling of multivariate time series.
This is a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970. It focuses on practical techniques throughout, rather than a rigorous mathematical treatment of the subject. It explores the building of stochastic (statistical) models for time series and their use in important areas of application —forecasting, model specification, estimation, and checking, transfer function modeling of dynamic relationships, modeling the effects of intervention events, and process control.
